From explainable NLP to quantum dynamics prediction

Title: From explainable NLP to quantum dynamics prediction: A two-way synergy between many-body quantum physics and temporal machine learning models
Speaker: Thiparat Chotibut from Chulalongkorn University
Abstract:
In this talk, we will discuss our recent work that highlights the fruitful interplay between many-body quantum physics and temporal machine learning models. The first part, "Quantum Meets Language," employs techniques from many-body quantum physics to enhance explainability in the common natural language processing task of sentiment analysis. We will examine how transforming a recurrent neural network model into its matrix product states counterpart can inform design principles and facilitate interpretable predictions in machine learning models for sentiment analysis [1]. The second part, "Forecasting Many-Body Quantum Dynamics with Machine Learning," delves into our data-driven approach that uses a variant of reservoir computing to accurately predict complex quantum many-body dynamics far into the future, circumventing the need for computing intermediate time steps that typically slow down classical simulations of such dynamics [2]. These findings not only demonstrate the capabilities of GPUs in advancing scientific research but also underscore the potential of these interdisciplinary approaches to research in AI, materials science, and quantum simulation.
References:
[1] J. Tangpanitanon et al, Explainable Natural Language Processing with Matrix Product States, New Journal of Physics, 24 053032, 2022
[2] A. Sornsaeng et al, Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Predicting Quantum
doi.org/10.485...
Bio: Thiparat is a theoretical and computational physicist at Chulalongkorn university working on the intersection of computer science and physics. He is leading Chula Intelligent and Complex Systems research unit and is also a Chief Global Officer and co-founder of Quantum Technology Foundation (Thailand). His work involves formulating and developing theoretical concepts and computational software to solve problems in science and mathematics, with the current focus on the analysis and the design of novel machine learning algorithms through the lens of computational neuroscience, statistical physics, and many-body quantum physics.